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Chest. 2018 Nov;154(5):1239-1248. doi: 10.1016/j.chest.2018.04.037. Epub 2018 May 9.

Big Data and Data Science in Critical Care.

Author information

1
Department of Pediatrics (Critical Care), Northwestern University Feinberg School of Medicine, Chicago, IL; Department of Preventive Medicine (Health and Biomedical Informatics), Northwestern University Feinberg School of Medicine, Chicago, IL.
2
Department of Preventive Medicine (Health and Biomedical Informatics), Northwestern University Feinberg School of Medicine, Chicago, IL.
3
Department of Medicine, The University of Chicago, Chicago, IL. Electronic address: matthew.churpek@uchospitals.edu.

Abstract

The digitalization of the health-care system has resulted in a deluge of clinical big data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the ICU, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, thus far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the ICU. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of big data and data science. The present article reviews the definitions, types of algorithms, applications, challenges, and future of big data and data science in critical care.

KEYWORDS:

big data; critical care; data science; machine learning; prediction models

PMID:
29752973
PMCID:
PMC6224705
DOI:
10.1016/j.chest.2018.04.037
[Indexed for MEDLINE]
Free PMC Article

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